Triple

T9514082
Position Surface form Disambiguated ID Type / Status
Subject Woodmere, Ohio E229477 entity
Predicate adjacentTo P224 FINISHED
Object Orange, Ohio E391681 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Orange, Ohio | Statement: [Woodmere, Ohio, adjacentTo, Orange, Ohio]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Orange, Ohio
Context triple: [Woodmere, Ohio, adjacentTo, Orange, Ohio]
  • A. Orange Township, Ohio chosen
    Orange Township, Ohio is a small community in Cuyahoga County best known as the birthplace of U.S. President James A. Garfield.
  • B. Hillsboro, Ohio
    Hillsboro, Ohio is a small city in Highland County known as a regional hub for the surrounding rural communities of southwestern Ohio.
  • C. Oxford, Ohio
    Oxford, Ohio is a small college town in southwestern Ohio best known as the home of Miami University.
  • D. Norwalk, Ohio
    Norwalk, Ohio is a small city in northern Ohio that serves as the county seat of Huron County and a regional hub for the surrounding rural communities.
  • E. Dublin, Ohio
    Dublin, Ohio is a suburban city northwest of Columbus known for its affluent neighborhoods, strong school system, and annual Dublin Irish Festival.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca84777560819084cddd999badc1aa completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd986bfcac8190aa97f8975cb17f6c completed April 1, 2026, 10:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69d13a3e93a481908569a4ec80c834ab completed April 4, 2026, 4:20 p.m.
Created at: March 30, 2026, 7:58 p.m.